1. Edge10 Workshop on Princeton Edge Lab’s 10th
Mung Chiang
May 17, 2019

2. Outline Ten years ago… Smart Data Pricing Edge/Fog Edge for Pricing
Pricing the Edge
Edge/Fog
SCALE
Interfaces
Fogonomics
Dispersive Learning

3. Acknowledgments Postdocs, students, visitors Collaborators
Funding agencies
Industry partners

4. 0. Ten Years Ago…

5. Research Bridget theory-practice gaps in networking
Proofs to prototypes
Edge/Fog (technological networks)
Smart Data Pricing (economic networks)
Social Learning Networks (social networks)

6. Education 2011: Network20Q & flip classroom
2012: MOOC (Chris) 400,000 students
2012: “Networked Life”
2016: “Power of Networks” (Chris)

7. Startups 2013: DataMi (Sangtae, Carlee, Soumya) 60 million users
2014: Zoomi (Sangtae, Ruediger, Chris)
2015: Smartiply (Junshan, Kaushik)
2017: Myota (Jaeyoon, Sangtae)

8. Industry and Community Impact
About a dozen company partners
2015: OpenFog Consortium
2018: Industrial Internet Consortium
Major conference panels, workshops, industry forums
Special journal/magazine issues and 2 edited books on Fog & SDP
~50 postdocs/Ph.D. students, ~25 as faculty and ~25 in industry

9. 1. Smart Data Pricing

10. SDP Dimensions How? Whom? What? More
Usage-based, demand response … real-time …
Whom?
Toll-free (1-800, zero rating, sponsored data, split billing)…
What?
App-based (no data plan), cloud pricing… IoT pricing, PMP…
More
Offloading, Quota-aware preloading…B2B, roaming, peering…
AT&T speed tiers

11. Example: Time Elasticity of Applications
Large Peak-Valley Differential
Streaming
videos,
Gaming
Texting,
Weather, Finance ,
Social Network updates
Cloud
Software
Downloads
Movies & Multimedia downloads,
P2P
Opportunities
Opportunities for
Exploiting time-elasticity
of demand

12. Cost-effective Mobile Content Delivery
Reduce peak & increase valley
Defer capital spending
Sell unused capacity
Increase revenue

13. Edge Complements Cloud
SDK

14. Rethink Spectrum
Flashy Whitespace

15. Rethink Ecosystem Stop (just) counting bytes and start living with QoE
Recognize, leverage heterogeneity of apps and networks
Win – Win – Win
Consumers: more choices and lower $/GB
Carriers: higher revenue and lower cost
Content and app providers: more engaged eyeballs

16. Rethink Networks End User Cellular Core Smart sharing in APP + PHY
Mobile management from the edge

17. Pricing 5G
Spectrum allocation/auctions for new bands of licensed and unlicensed spectrum
Infrastructure sharing:  given densification, how will resource sharing work between competitive operators?
Pricing of consumer mobile
Pricing for broadband access
Pricing of industrial IoT
How will these pricing options evolve when killer apps emerge and mmWave devices become affordable?
Taken from JSAC special issue proposal.

18. Pricing IoT How to charge? Whom to charge?
Time-dependent? Volume discounts?
Application-dependent pricing: pricing with guarantees on delivered outcome or experience, e.g. price 5G network slices with guaranteed QoS
Whom to charge?
Stakeholders include IoT service provider (e.g. smart home sensors), IoT wireless access provider (e.g AT&T), and IoT cloud platforms (e.g. Amazon AWS IoT Hub)
Whom should users pay? Users pay each separately vs users pay only service provider vs …
Vertical integration of stakeholders
What happens if AT&T or Verizon offer both IoT management platforms and connectivity? (they do; Verizon ThingSpace and AT&T Control Center)

19. 2. Edge/Fog

20. Distribute functions to network edge
2009
Distribute functions to network edge

21. Distribute functions along Cloud-2-Things Continuum
Distribute functions along Cloud-2-Things Continuum

22. To Fog or Not to Fog: SCALE
Security
Cognition
Agility
Latency
Efficiency

23. Fog as An Architecture Architecture is “Horizontal Foundation”:
Who does what, at what timescale, how to glue them together?
Allocation of functions, not just resources
Architecture supports Applications:
Source-channel separation: Digital communication
TCP/IP: Internet applications
Fog/edge: IoT / 5G / Dispersive AI

24. A. Interfaces Massive storage Real time processing
Heavy duty computation Global coordination
Wide-area connectivity
Real time processing
Rapid innovation
Client-centric
Edge resource pooling

25. Example: Shred and Spread
Client-driven data processing for privacy protection and reliability
Scatter files to multiple fog storages
Client-side data deduplication
Obfuscated data in storages
File chunking for data deduplication
Chunk encoding/spreading for privacy and reliability

26. Example: Networked Drone Cameras

27. S S B. Fogonomics Compute price: Memory size Compute time Data storage
Communication price:
Requests across functions
Data transmission
Internet access
Incentivizing local dispersed resources:
Cellular data plans
User mobility pattern
Heterogeneous devices
Network connections

28. Application-Dependent Pricing
The specific application offered changes resources and pricing
Example: Pricing of data collected by edge devices
Optimal amount and frequency of charging
Pricing based on measures of freshness of data
How to price and sell private data?
Example: Pricing of distributed ML services
Using fog/edge resources for distributed ML to make inferences, find correlations, or for online planning

29. C. Edge/Fog for Dispersive AI
Design machine learning algorithms that support fast responses
Decompose machine learning into multiple geographically distributed components (jointly operating to adaptively optimize data collection/analytics)
Minimize communication costs and centralized data processing costs
Make best use of local/proximal resources
Proactively pre-position content and computing
Parallelize successive refinement for streaming mining
Reduce infrastructure costs and improve quality of experience

30. Dispersive Learning
Decentralized, online decision making under uncertainty by a team of edge devices in an unknown environment
Examples: fleet of drones deployed for anti-poaching efforts, team of disaster relief robots
Solution approach: multi-agent reinforcement learning, augmented with inter- agent communication for better learning and coordination
Information shared by the informed devices with others could in fact degrade their learning early on
Delayed sharing may be preferred: wait until policies have improved, then share

31. Information sharing might help learning…
Timing Matters
Information sharing might help learning…
Or might degrade it!
P. Naghizadeh, M. Gorlatova, A. Lan, M. Chiang. “Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning”, INFOCOM 2019.

32. Unique Challenges & Opportunities
Heterogeneity/Under-organization of resources/devices
Variability/Volatility in availability/mobility
Constraints in bandwidth/battery
Proximity to sensors/actuators

33. Thank you & To the next 10 years chiang@purdue. edu chiangm@princeton
Thank you & To the next 10 years